In-context learning [1]} generally refers to adapting to a task by providing training examples as additional textual input, without performing gradient-based updates. This technique
imposes a limit on size of the training dataset due to context length limits. Recent work from [2]} and [3]} demonstrate that the choice of in-context training examples, and the order in which they are presented have large effects on performance.
